pytorch-forecasting 0.3.1

Forecasting timeseries with PyTorch - dataloaders, normalizers, metrics and models

Pytorch Forecasting aims to ease timeseries forecasting with neural networks for real-world cases and research alike. Specifically, the package provides

• A timeseries dataset class which abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc.
• A base model class which provides basic training of timeseries models along with logging in tensorboard and generic visualizations such actual vs predictions and dependency plots
• Multiple neural network architectures for timeseries forecasting that have been enhanced for real-world deployment and come with in-built interpretation capabilities
• Multi-horizon timeseries metrics
• Ranger optimizer for faster model training
• Hyperparameter tuning with optuna

The package is built on [pytorch-lightning])(https://pytorch-lightning.readthedocs.io/) to allow training on CPUs, single and multiple GPUs out-of-the-box.

Installation

If you are working windows, you need to first install PyTorch with

pip install torch -f https://download.pytorch.org/whl/torch_stable.html.

Otherwise, you can proceed with

pip install pytorch-forecasting

Visit the documentation at https://pytorch-forecasting.readthedocs.io.

Available models

• Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
• N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Usage

import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping

from pytorch_forecasting import TimeSeriesDataSet, TemporalFusionTransformer

# load data
data = ...

# define dataset
max_encode_length = 36
max_prediction_length = 6
training_cutoff = "YYYY-MM-DD"  # day for cutoff

training = TimeSeriesDataSet(
    data[lambda x: x.date <= training_cutoff],
    time_idx= ...,
    target= ...,
    group_ids=[ ... ],
    max_encode_length=max_encode_length,
    max_prediction_length=max_prediction_length,
    static_categoricals=[ ... ],
    static_reals=[ ... ],
    time_varying_known_categoricals=[ ... ],
    time_varying_known_reals=[ ... ],
    time_varying_unknown_categoricals=[ ... ],
    time_varying_unknown_reals=[ ... ],
)


validation = TimeSeriesDataSet.from_dataset(training, data, min_prediction_idx=training.index.time.max() + 1, stop_randomization=True)
batch_size = 128
train_dataloader = training.to_dataloader(train=True, batch_size=batch_size, num_workers=2)
val_dataloader = validation.to_dataloader(train=False, batch_size=batch_size, num_workers=2)


early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=1e-4, patience=1, verbose=False, mode="min")
lr_logger = LearningRateLogger()
trainer = pl.Trainer(
    max_epochs=100,
    gpus=0,
    gradient_clip_val=0.1,
    early_stop_callback=early_stop_callback,
    limit_train_batches=30,
    callbacks=[lr_logger],
)


tft = TemporalFusionTransformer.from_dataset(
    training,
    learning_rate=0.03,
    hidden_size=32,
    attention_head_size=1,
    dropout=0.1,
    hidden_continuous_size=16,
    output_size=7,
    loss=QuantileLoss(),
    log_interval=2,
    reduce_on_plateau_patience=4
)
print(f"Number of parameters in network: {tft.size()/1e3:.1f}k")

# find optimal learning rate
res = trainer.lr_find(
    tft, train_dataloader=train_dataloader, val_dataloaders=val_dataloader, early_stop_threshold=1000.0, max_lr=0.3,
)

print(f"suggested learning rate: {res.suggestion()}")
fig = res.plot(show=True, suggest=True)
fig.show()

trainer.fit(
    tft, train_dataloader=train_dataloader, val_dataloaders=val_dataloader,
)